B-cell receptor signaling activity identifies patients with mantle cell lymphoma at higher risk of progression.

Mantle cell lymphoma (MCL) is an incurable B-cell malignancy characterized by a high clinical variability. Therefore, there is a critical need to define parameters that identify high-risk patients for aggressive disease and therapy resistance. B-cell receptor (BCR) signaling is crucial for MCL initiation and progression and is a target for therapeutic intervention. We interrogated BCR signaling proteins (SYK, LCK, BTK, PLCγ2, p38, AKT, NF-κB p65, and STAT5) in 30 primary MCL samples using phospho-specific flow cytometry. Anti-IgM modulation induced heterogeneous BCR signaling responses among samples allowing the identification of two clusters with differential responses. The cluster with higher response was associated with shorter progression free survival (PFS) and overall survival (OS). Moreover, higher constitutive AKT activity was predictive of inferior response to the Bruton's tyrosine kinase inhibitor (BTKi) ibrutinib. Time-to-event analyses showed that MCL international prognostic index (MIPI) high-risk category and higher STAT5 response were predictors of shorter PFS and OS whilst MIPI high-risk category and high SYK response predicted shorter OS. In conclusion, we identified BCR signaling properties associated with poor clinical outcome and resistance to ibrutinib, thus highlighting the prognostic and predictive significance of BCR activity and advancing our understanding of signaling heterogeneity underlying clinical behavior of MCL.

Prognostic Biomarkers for Hepatic Veno-Occlusive Disease/Sinusoidal Obstruction Syndrome in Myeloablative Allogeneic Hematopoietic Cell Transplantation: Results from the Blood and Marrow Transplant Clinical Trials Network 1202 Study.

Hepatic veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is a potentially life-threatening complication of hematopoietic cell transplantation (HCT). This study aimed to determine a blood biomarker signature early post-HCT that identifies patients at high risk for VOD/SOS. A set of 23 plasma biomarkers, selected from the VOD/SOS literature, was measured on days 0, 7, and 14 after myeloablative HCT using blood samples from patients enrolled in the Blood and Marrow Transplant Clinical Trials Network (BMT CTN) Protocol 1202. Eligible cases were diagnosed with VOD/SOS in BMT CTN 1202 using the Baltimore criteria. Controls (without VOD/SOS) were matched to cases for conditioning regimen and age. Significant biomarkers were identified using the Bonferroni-adjusted Wilcoxon rank-sum test (P ≤ .002). Thirty-three patients with mild or severe VOD/SOS were identified (cases) and matched to 107 controls. Two, 8, and 5 biomarkers measured from the plasma of these patients were significantly associated with the development of VOD/SOS at days 0, 7, and 14, respectively, with the strongest associations on days 7 and 14. Biomarker associations were stronger for severe VOD/SOS risk and were stronger prognostic markers for VOD/SOS cases occurring within 28 days of HCT. Hyaluronan was most strongly associated with VOD/SOS risk, with an area under the receiver operating characteristic curve (AUC) of .81 on day 7 and .79 on day 14. Multivariate models of up to 5 biomarkers generated AUCs ranging from .82 to .85. All associations with VOD/SOS risk were independent of clinical risk factors. This study confirms previously identified biomarkers of VOD/SOS risk and identified novel prognostic biomarker signatures that identify patients at risk for VOD/SOS shortly after HCT. Multivariate analysis suggests that a combination of up to 5 of these protein biomarkers may provide a prognostic tool for identifying patients at risk for VOD/SOS.

Cell by cell immuno- and cancer marker profiling of non-small cell lung cancer tissue: Checkpoint marker expression on CD103+, CD4+ T-cells predicts circulating tumor cells.

Non-small cell lung cancer (NSCLC) has a poor prognosis. Targeted therapy and immunotherapy in recent years has significantly improved NSCLC patient outcome. In this study, we employed cell-by-cell immune and cancer marker profiling of the primary tumor cells to investigate possible signatures that might predict the presence or absence of circulating tumor cells (CTCs). We performed a comprehensive study on 10 NSCLC patient tissue samples with paired blood samples. The solid tissue biopsy samples were dissociated into single cells by non-enzymatic tissue homogenization and stained with a total 25 immune, cancer markers and DNA content dye and analyzed with high-parameter flow cytometry. CTCs were isolated and analyzed from the paired peripheral blood. We investigated a total of 74 biomarkers for their correlation with CTC number. Strong correlations were observed between CTC number and the frequency of immune checkpoint marker expressing lymphocytes (CTLA-4, LAG3, TIM3, PD-1), within the CD103+CD4+ T lymphocyte subset. CTC number is also correlated with the frequency of PD-L1 expressing cancer cells and cancer cell DNA content. In contrast, CTC number inversely correlated to the frequency of CD44+E-cadherin- cancer cells. Unsupervised clustering analysis based on the biomarker analysis separated the CTC negative patients from the CTC positive patients. Profiling multiple immune and cancer markers on cancer samples with multi-parametric flow cytometry allowed us to obtain protein expression information at the single cell level. Clustering analysis of the proteomic data revealed a signature driven by checkpoint marker expression on CD103+CD4+ T cells that could potentially be predictive of CTCs and targets of therapy.

Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia.

Single-cell network profiling (SCNP) data generated from multi-parametric flow cytometry analysis of bone marrow (BM) and peripheral blood (PB) samples collected from patients >55 years old with non-M3 AML were used to train and validate a diagnostic classifier (DXSCNP) for predicting response to standard induction chemotherapy (complete response [CR] or CR with incomplete hematologic recovery [CRi] versus resistant disease [RD]). SCNP-evaluable patients from four SWOG AML trials were randomized between Training (N = 74 patients with CR, CRi or RD; BM set = 43; PB set = 57) and Validation Analysis Sets (N = 71; BM set = 42, PB set = 53). Cell survival, differentiation, and apoptosis pathway signaling were used as potential inputs for DXSCNP. Five DXSCNP classifiers were developed on the SWOG Training set and tested for prediction accuracy in an independent BM verification sample set (N = 24) from ECOG AML trials to select the final classifier, which was a significant predictor of CR/CRi (area under the receiver operating characteristic curve AUROC = 0.76, p = 0.01). The selected classifier was then validated in the SWOG BM Validation Set (AUROC = 0.72, p = 0.02). Importantly, a classifier developed using only clinical and molecular inputs from the same sample set (DXCLINICAL2) lacked prediction accuracy: AUROC = 0.61 (p = 0.18) in the BM Verification Set and 0.53 (p = 0.38) in the BM Validation Set. Notably, the DXSCNP classifier was still significant in predicting response in the BM Validation Analysis Set after controlling for DXCLINICAL2 (p = 0.03), showing that DXSCNP provides information that is independent from that provided by currently used prognostic markers. Taken together, these data show that the proteomic classifier may provide prognostic information relevant to treatment planning beyond genetic mutations and traditional prognostic factors in elderly AML.

High-dimensional analysis of the aging immune system: verification of age-associated differences in immune signaling responses in healthy donors.

BACKGROUND: Single-cell network profiling (SCNP) is a multiparametric flow cytometry-based approach that simultaneously measures evoked signaling in multiple cell subsets. Previously, using the SCNP approach, age-associated immune signaling responses were identified in a cohort of 60 healthy donors. METHODS: In the current study, a high-dimensional analysis of intracellular signaling was performed by measuring 24 signaling nodes in 7 distinct immune cell subsets within PBMCs in an independent cohort of 174 healthy donors [144 elderly (>65 yrs); 30 young (25-40 yrs)]. RESULTS: Associations between age and 9 immune signaling responses identified in the previously published 60 donor cohort were confirmed in the current study. Furthermore, within the current study cohort, 48 additional immune signaling responses differed significantly between young and elderly donors. These associations spanned all profiled modulators and immune cell subsets. CONCLUSIONS: These results demonstrate that SCNP, a systems-based approach, can capture the complexity of the cellular mechanisms underlying immunological aging. Further, the confirmation of age associations in an independent donor cohort supports the use of SCNP as a tool for identifying reproducible predictive biomarkers in areas such as vaccine response and response to cancer immunotherapies.

Development and validation of a single-cell network profiling assay-based classifier to predict response to induction therapy in paediatric patients with de novo acute myeloid leukaemia: a report from the Children's Oncology Group.

Single cell network profiling (SCNP) is a multi-parameter flow cytometry technique for simultaneous interrogation of intracellular signalling pathways. Diagnostic paediatric acute myeloid leukaemia (AML) bone marrow samples were used to develop a classifier for response to induction therapy in 53 samples and validated in an independent set of 68 samples. The area under the curve of a receiver operating characteristic curve (AUC(ROC)) was calculated to be 0·85 in the training set and after exclusion of induction deaths, the AUC(ROC) of the classifier was 0·70 (P = 0·02) and 0·67 (P = 0·04) in the validation set when induction deaths (intent to treat) were included. The highest predictive accuracy was noted in the cytogenetic intermediate risk patients (AUC(ROC) 0·88, P = 0·002), a subgroup that lacks prognostic/predictive biomarkers for induction response. Only white blood cell count and cytogenetic risk were associated with response to induction therapy in the validation set. After controlling for these variables, the SCNP classifier score was associated with complete remission (P = 0·017), indicating that the classifier provides information independent of other clinical variables that were jointly associated with response. This is the first validation of an SCNP classifier to predict response to induction chemotherapy. Herein we demonstrate the usefulness of quantitative SCNP under modulated conditions to provide independent information on AML disease biology and induction response.

Functional pathway analysis using SCNP of FLT3 receptor pathway deregulation in AML provides prognostic information independent from mutational status.

FMS-like tyrosine kinase 3 receptor (FLT3) internal tandem duplication (ITD) mutations result in constitutive activation of this receptor and have been shown to increase the risk of relapse in patients with acute myeloid leukemia (AML); however, substantial heterogeneity in clinical outcomes still exists within both the ITD mutated and unmutated AML subgroups, suggesting alternative mechanisms of disease relapse not accounted by FLT3 mutational status. Single cell network profiling (SCNP) is a multiparametric flow cytometry based assay that simultaneously measures, in a quantitative fashion and at the single cell level, both extracellular surface marker levels and changes in intracellular signaling proteins in response to extracellular modulators. We previously reported an initial characterization of FLT3 ITD-mediated signaling using SCNP. Herein SCNP was applied sequentially to two separate cohorts of samples collected from elderly AML patients at diagnosis. In the first (training) study, AML samples carrying unmutated, wild-type FLT3 (FLT3 WT) displayed a wide range of induced signaling, with a fraction having signaling profiles comparable to FLT3 ITD AML samples. Conversely, the FLT3 ITD AML samples displayed more homogeneous induced signaling, with the exception of patients with low (<40%) mutational load, which had profiles comparable to FLT3 WT AML samples. This observation was then confirmed in an independent (verification) cohort. Data from the second cohort were also used to assess the association between SCNP data and disease-free survival (DFS) in the context of FLT3 and nucleophosmin (NPM1) mutational status among patients who achieved complete remission (CR) to induction chemotherapy. The combination of SCNP read outs together with FLT3 and NPM1 molecular status improved the DFS prediction accuracy of the latter. Taken together, these results emphasize the value of comprehensive functional assessment of biologically relevant signaling pathways in AML as a basis for the development of highly predictive tests for guidance of post-remission therapy.

AKT signaling as a novel factor associated with in vitro resistance of human AML to gemtuzumab ozogamicin.

Gemtuzumab ozogamicin (GO), an immunoconjugate between an anti-CD33 antibody and a calicheamicin-γ(1) derivative, induces remissions and improves survival in a subset of patients with acute myeloid leukemia (AML). As the mechanisms underlying GO and calicheamicin-γ(1) resistance are incompletely understood, we herein used flow cytometry-based single cell network profiling (SCNP) assays to study cellular responses of primary human AML cells to GO. Our data indicate that the extent of DNA damage is quantitatively impacted by CD33 expression and drug efflux activity. However, although DNA damage is required for GO-induced cytotoxicity, it is not sufficient for effective cell kill, suggesting that downstream anti-apoptotic pathways may function as relevant resistance mechanisms. Supporting this notion, we found activated PI3K/AKT signaling to be associated with GO resistance in vitro in primary AML cells. Consistently, the investigational AKT inhibitor MK-2206 significantly sensitized various human AML cells to GO or free calicheamicin-γ(1) with particularly pronounced effects in otherwise GO or free calicheamicin-γ(1)-resistant cells. Likewise, MK-2206 also sensitized primary AML cells to calicheamicin-γ(1). Together, our findings illustrate the capacity of SCNP assays to discover chemotherapy-related biological pathways and signaling networks relevant to GO-induced genotoxic stress. The identification of AKT signaling as being associated with GO resistance in vitro may provide a novel approach to improve the in vivo efficacy of GO/calicheamicin-γ(1) and, by extrapolation, other DNA damage-based therapeutics.

Functional pathway analysis in acute myeloid leukemia using single cell network profiling assay: effect of specimen source (bone marrow or peripheral blood) on assay readouts.

BACKGROUND: Single cell network profiling (SCNP) is used to simultaneously measure the effects of modulators on signaling networks at the single cell level. SCNP-based biomarker assays predictive of response to induction therapy and relapse risk in acute myeloid leukemia (AML) patients are being developed. Such assays have typically used bone marrow (BM) as the sample source of blasts. Because circulating peripheral blasts are detectable in ∼65% of AML patients and peripheral blood (PB) sampling is less invasive than BM sampling, this study was performed to assess the effect of sample source on AML blasts signaling as measured in SCNP assay. METHODS: SCNP using multiparametric flow cytometry was used to evaluate the activation state of intracellular signaling molecules in leukemic blasts under basal conditions and after treatment with modulators in 46 pairs of BM mononuclear cells/PB mononuclear cells. The relationship between readouts of modulated intracellular proteins ("nodes") was measured using linear regression, Bland-Altman method, and Lin's concordance correlation coefficient. RESULTS: The majority (156/161) of signaling nodes show strong correlations between paired PB and BM samples independently from the statistical method used. Notable exceptions were two PB samples with almost undetectable levels of circulating blasts compared with paired BM samples. CONCLUSIONS: Our results demonstrate that specimen source (BM or PB) does not significantly affect proteomic signaling in patients with AML and circulating blasts. The ability to use PB as a sample source will facilitate the monitoring of cellular signaling effects following administration of targeted therapies and at time points when BM aspirates are not clinically justifiable.

Single-cell network profiling of peripheral blood mononuclear cells from healthy donors reveals age- and race-associated differences in immune signaling pathway activation.

A greater understanding of the function of the human immune system at the single-cell level in healthy individuals is critical for discerning aberrant cellular behavior that occurs in settings such as autoimmunity, immunosenescence, and cancer. To achieve this goal, a systems-level approach capable of capturing the response of the interdependent immune cell types to external stimuli is required. In this study, an extensive characterization of signaling responses in multiple immune cell subpopulations within PBMCs from a cohort of 60 healthy donors was performed using single-cell network profiling (SCNP). SCNP is a multiparametric flow cytometry-based approach that enables the simultaneous measurement of basal and evoked signaling in multiple cell subsets within heterogeneous populations. In addition to establishing the interindividual degree of variation within a broad panel of immune signaling responses, the possible association of any observed variation with demographic variables including age and race was investigated. Using half of the donors as a training set, multiple age- and race-associated variations in signaling responses in discrete cell subsets were identified, and several were subsequently confirmed in the remaining samples (test set). Such associations may provide insight into age-related immune alterations associated with high infection rates and diminished protection following vaccination and into the basis for ethnic differences in autoimmune disease incidence and treatment response. SCNP allowed for the generation of a functional map of healthy immune cell signaling responses that can provide clinically relevant information regarding both the mechanisms underlying immune pathological conditions and the selection and effect of therapeutics.

Functional characterization of FLT3 receptor signaling deregulation in acute myeloid leukemia by single cell network profiling (SCNP).

BACKGROUND: Molecular characterization of the FMS-like tyrosine kinase 3 receptor (FLT3) in cytogenetically normal acute myeloid leukemia (AML) has recently been incorporated into clinical guidelines based on correlations between FLT3 internal tandem duplications (FLT3-ITD) and decreased disease-free and overall survival. These mutations result in constitutive activation of FLT3, and FLT3 inhibitors are currently undergoing trials in AML patients selected on FLT3 molecular status. However, the transient and partial responses observed suggest that FLT3 mutational status alone does not provide complete information on FLT3 biological activity at the individual patient level. Examination of variation in cellular responsiveness to signaling modulation may be more informative. METHODOLOGY/PRINCIPAL FINDINGS: Using single cell network profiling (SCNP), cells were treated with extracellular modulators and their functional responses were quantified by multiparametric flow cytometry. Intracellular signaling responses were compared between healthy bone marrow myeloblasts (BMMb) and AML leukemic blasts characterized as FLT3 wild type (FLT3-WT) or FLT3-ITD. Compared to healthy BMMb, FLT3-WT leukemic blasts demonstrated a wide range of signaling responses to FLT3 ligand (FLT3L), including elevated and sustained PI3K and Ras/Raf/Erk signaling. Distinct signaling and apoptosis profiles were observed in FLT3-WT and FLT3-ITD AML samples, with more uniform signaling observed in FLT3-ITD AML samples. Specifically, increased basal p-Stat5 levels, decreased FLT3L induced activation of the PI3K and Ras/Raf/Erk pathways, decreased IL-27 induced activation of the Jak/Stat pathway, and heightened apoptotic responses to agents inducing DNA damage were observed in FLT3-ITD AML samples. Preliminary analysis correlating these findings with clinical outcomes suggests that classification of patient samples based on signaling profiles may more accurately reflect FLT3 signaling deregulation and provide additional information for disease characterization and management. CONCLUSIONS/SIGNIFICANCE: These studies show the feasibility of SCNP to assess modulated intracellular signaling pathways and characterize the biology of individual AML samples in the context of genetic alterations.

Distinct patterns of DNA damage response and apoptosis correlate with Jak/Stat and PI3kinase response profiles in human acute myelogenous leukemia.

BACKGROUND: Single cell network profiling (SCNP) utilizing flow cytometry measures alterations in intracellular signaling responses. Here SCNP was used to characterize Acute Myeloid Leukemia (AML) disease subtypes based on survival, DNA damage response and apoptosis pathways. METHODOLOGY AND PRINCIPAL FINDINGS: Thirty four diagnostic non-M3 AML samples from patients with known clinical outcome were treated with a panel of myeloid growth factors and cytokines, as well as with apoptosis-inducing agents. Analysis of induced Jak/Stat and PI3K pathway responses in blasts from individual patient samples identified subgroups with distinct signaling profiles that were not seen in the absence of a modulator. In vitro exposure of patient samples to etoposide, a DNA damaging agent, revealed three distinct "DNA damage response (DDR)/apoptosis" profiles: 1) AML blasts with a defective DDR and failure to undergo apoptosis; 2) AML blasts with proficient DDR and failure to undergo apoptosis; 3) AML blasts with proficiency in both DDR and apoptosis pathways. Notably, AML samples from clinical responders fell within the "DDR/apoptosis" proficient profile and, as well, had low PI3K and Jak/Stat signaling responses. In contrast, samples from clinical non responders had variable signaling profiles often with in vitro apoptotic failure and elevated PI3K pathway activity. Individual patient samples often harbored multiple, distinct, leukemia-associated cell populations identifiable by their surface marker expression, functional performance of signaling pathway in the face of cytokine or growth factor stimulation, as well as their response to apoptosis-inducing agents. CONCLUSIONS AND SIGNIFICANCE: Characterizing and tracking changes in intracellular pathway profiles in cell subpopulations both at baseline and under therapeutic pressure will likely have important clinical applications, potentially informing the selection of beneficial targeted agents, used either alone or in combination with chemotherapy.

Dynamic single-cell network profiles in acute myelogenous leukemia are associated with patient response to standard induction therapy.

PURPOSE: Complete response to induction chemotherapy is observed in approximately 60% of patients with newly diagnosed non-M3 acute myelogenous leukemia (AML). However, no methods exist to predict with high accuracy at the individual patient level the response to standard AML induction therapy. EXPERIMENTAL DESIGN: We applied single-cell network profiling (SCNP) using flow cytometry, a tool that allows a comprehensive functional assessment of intracellular signaling pathways in heterogeneous tissues, to two training cohorts of AML samples (n = 34 and 88) to predict the likelihood of response to induction chemotherapy. RESULTS: In the first study, univariate analysis identified multiple signaling "nodes" (readouts of modulated intracellular signaling proteins) that correlated with response (i.e., AUC(ROC) > or = 0.66; P < or = 0.05) at a level greater than age. After accounting for age, similar findings were observed in the second study. For patients <60 years old, complete response was associated with the presence of intact apoptotic pathways. In patients > or =60 years old, nonresponse was associated with FLT3 ligand-mediated increase in phosphorylated Akt and phosphorylated extracellular signal-regulated kinase. Results were independent of cytogenetics, FLT3 mutational status, and diagnosis of secondary AML. CONCLUSIONS: These data emphasize the value of performing quantitative SCNP under modulated conditions as a basis for the development of tests highly predictive for response to induction chemotherapy. SCNP provides information distinct from other known prognostic factors such as age, secondary AML, cytogenetics, and molecular alterations and is potentially combinable with the latter to improve clinical decision making. Independent validation studies are warranted.

Single cell network profiling (SCNP): mapping drug and target interactions.

Measuring target coverage of small molecule inhibitors is paramount-first, for selection of molecules to progress through the drug development process and second, once a candidate drug moves to clinical testing, for guiding dose/schedule selection. Single cell network profiling (SCNP) using multiparameter flow cytometry can measure compound effects on multiple signaling cascades in a cell-type-specific manner. We applied SCNP to a panel of compounds with reported inhibitory effects on Jak/Stat signaling using a novel system where modulation of multiple signaling cascades are simultaneously measured in discrete cell subsets in whole (ie, unfractionated) blood. Jak2 vs. Jak3 selectivity as well as "off-target" effects on other cell signaling pathways were measured using a combination of cytokines that target different white blood cell subsets, namely GM-CSF (monocytes/granulocytes), IL-2 (T cells), and CD40L (B cells). The compounds were then rank-ordered by potency and selectivity against the different pathways tested. Notably, SCNP performed in whole unfractionated blood compared to fractionated peripheral blood mononuclear cells (PBMC) from the same donors revealed potency loss for all compounds, with one exception. These studies show that SCNP can be used to efficiently measure a drug candidate's potency and selectivity in a physiologically relevant environment (eg, whole blood) and that robust IC(50) are attainable from rare subpopulations (<100 cells). The ability to generate in vitro IC(50) measurements in whole blood can be used not only for the preclinical selection of lead molecules, but also to determine the target plasma concentration for clinical studies and to measure target coverage after drug administration in early phase clinical trials. Knowledge of the compound plasma concentration necessary to achieve biochemical coverage permits rational design of clinical trials based on biologically active dose vs. the traditional maximum tolerated dose (MTD) design, which is better suited for cytotoxic, nontargeted drugs.

Shapes of things: computer modeling of molecular shape in drug discovery.

We review recent advances in computer modeling of molecular shape in drug discovery. We summarize the ways of representing shape computationally, discuss the various means of aligning molecules and shapes, consider the various ways of scoring similarity of shapes, and describe the ways in which these shapes can be used to construct molecular descriptors. Finally, we evaluate the success of these methods to date, suggest when they are best applied, and provide our recommendations for the direction of future work.

Feature-map vectors: a new class of informative descriptors for computational drug discovery.

In order to develop robust machine-learning or statistical models for predicting biological activity, descriptors that capture the essence of the protein-ligand interaction are required. In the absence of structural information from X-ray or NMR experiments, deriving informative descriptors can be difficult. We have developed feature-map vectors (FMVs), a new class of descriptors based on chemical features, to address this challenge. FMVs, which are derived from the conformational models of a few actives, are low dimensional, problem specific, and highly interpretable. By using shape-based alignments and scoring with chemical features, FMVs can combine information about a molecule's shape and the pharmacophores it can match. In five validation studies, bag classifiers built using FMVs have shown high enrichments for identifying actives for five diverse targets: CDK2, 5-HT(3), DHFR, thrombin, and ACE. The interpretability of these descriptors has been demonstrated for CDK2 and 5-HT(3), where the method automatically discovers the standard literature pharmacophore.

Conformation mining: an algorithm for finding biologically relevant conformations.

Discovering essential features shared by active compounds, an important step in drug-design, is complicated by conformational flexibility. We present a new algorithm to efficiently mine the conformational space of multiple actives and find small subsets of conformations likely to be biologically relevant. The approach identifies chemical and steric similarities between actives, providing insight into features important for binding when structural data are absent. Validation studies (thrombin and CDK2 data) produce alignments similar to protein-based alignments.

Building predictive ADMET models for early decisions in drug discovery.

This review discusses the current challenges facing researchers developing computational models to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) for early drug discovery. The strengths and weaknesses of different modeling approaches are reviewed and a survey of recent strategies to model several key ADMET parameters, including intestinal permeability, blood-brain barrier penetration, metabolism, bioavailability and drug toxicities, is presented.

Performance of 3D-database molecular docking studies into homology models.

The performance of docking studies into protein active sites constructed by homology model building was investigated using CDK2 and factor VIIa screening data sets. When the sequence identity between model and template near the binding site area is greater than approximately 50%, roughly 5 times more active compounds are identified than would be found randomly. This performance is comparable to docking to crystal structures.